Date: Wed, 20 Nov 1996 22:11:30 GMT
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<HEAD><TITLE>Numerical Linear Algebra</TITLE></HEAD>
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<H2>Numerical Linear Algebra</H2>
<H4>(Computer Science 106)</H4>

<EM>Identical to Engineering Sciences 104</EM><P>

<B>Times:</B> 98S: Arrange <BR>
<B>Prerequisite:</B>  <!WA0><A HREF="http://www.cs.dartmouth.edu/courseguide/undergrad/cs_26.html">Computer Science 26</A>, Mathematics 26, or Engineering Science 69. Students are to be familiar
with approximation theory, error analysis, direct and iterative techniques for solving linear systems, and
discretization of continuous problems to the level normally encountered in an undergraduate course in
numerical analysis. <P>


The course examines in the context of modern computational practice algorithms for solving linear systems
<CODE>Ax = b</CODE> and <CODE>Az = lx</CODE>. Matrix decomposition algorithms, matrix inversion, and eigenvector expansions are
studied. Algorithms for special matrix classes are featured, including symmetric positive definite
matrices, banded matrices, and sparse matrices. Error analysis and complexity analysis of the algorithms
are covered. 
The algorithms are implemented for selected examples chosen from elimination methods (linear systems),
least squares (filters), linear programming, incidence matrices (networks and graphs), diagonalization
(convolution), sparse matrices (partial differential equations). 


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